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NBSOM: The naive Bayes self-organizing map

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Abstract

The naive Bayes model has proven to be a simple yet effective model, which is very popular for pattern recognition applications such as data classification and clustering. This paper explores the possibility of using this model for multidimensional data visualization. To achieve this, a new learning algorithm called naive Bayes self-organizing map (NBSOM) is proposed to enable the naive Bayes model to perform topographic mappings. The training is carried out by means of an online expectation maximization algorithm with a self-organizing principle. The proposed method is compared with principal component analysis, self-organizing maps, and generative topographic mapping on two benchmark data sets and a real-world image processing application. Overall, the results show the effectiveness of NBSOM for multidimensional data visualization.

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Acknowledgments

The authors would like to thank the EU-funded I*PROMS Network of Excellence and the ORS Award for financially supporting this research.

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Authors and Affiliations

  1. Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Av. Diagonal las Torres 2640, Peñalolén, Santiago, Chile

    Gonzalo A. Ruz

  2. Manufacturing Engineering Centre, Cardiff University, Cardiff, CF24 3AA, UK

    Duc Truong Pham

  3. Department of Information Systems, College of Computer and Information Sciences, King Saud University, KSA, Riyadh, Saudi Arabia

    Duc Truong Pham

Authors
  1. Gonzalo A. Ruz
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  2. Duc Truong Pham
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Correspondence to Gonzalo A. Ruz.

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Ruz, G.A., Pham, D.T. NBSOM: The naive Bayes self-organizing map. Neural Comput & Applic 21, 1319–1330 (2012). https://doi.org/10.1007/s00521-011-0567-9

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  • DOI: https://doi.org/10.1007/s00521-011-0567-9

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